基于数据挖掘与需求响应的个性化智能用电套餐研究  

Research on intelligent electricity package based on deep mining and demand response

作　　者：丛小涵 苏慧玲 李海思 王蓓蓓[1] CONG Xiaohan;SU Huiling;LI Haisi;WANG Beibei(School of Electrical Engineering,Southeast University,Nanjing 210096,China;Research Institute Metering Center,State Grid Jiangsu Electric Power Co.,Ltd.,Nanjing 211100,China;Huzhou Power Supply Company,State Grid Zhejiang Electric Power Co.,Ltd.,Huzhou 313000,China)

机构地区：[1]东南大学电气工程学院,南京210096 [2]国网江苏省电力有限公司电力科学研究院计量中心,南京211100 [3]国网浙江省电力有限公司湖州供电公司,浙江湖州313000

出　　处：《电力需求侧管理》2019年第5期21-25,共5页Power Demand Side Management

基　　金：国家自然科学基金项目(71471036);国家电网公司科技项目(SGTYHT/16-JS-198)

摘　　要：在电力体制改革的背景下,有必要精细化挖掘用户用电特性,同时考虑售电商偏差考核控制的问题,制定套餐优化需求响应策略。首先基于自编码神经网络和模糊C均值聚类的方法对用户用电曲线进行模式分类,然后基于消费者心理学用户响应模型,对用户不同用电模式建立峰谷分时电价优化模型,在此基础上,对不同用电模式建立峰平时段叠加电价模型。研究表明,套餐制定可以有效引导用户调整用电行为,降低用电模式间差异,从偏差考核的角度看,有助于制定月购电策略。Under the background of power system reform,it is necessary to mine the user electricity characteristic and develop corresponding packages to optimize the demand response strategy considering the problem of the deviation assessment of load server entities.Firstly,the user’s power consumption curve based on self?coding neural network and fuzzy C?means clustering method is clas?sified.Then an optimization model of time?of?use electricity price based on the user response model of consumer psychology is estab?lished.Furtherly,a superimposed electricity price model for the peak period is established.Research shows that the development of package can effectively guide users to adjust their electricity con?sumption behavior,so as to reduce the difference between electrici?ty consumption modes.From the perspective of deviation assess?ment,it is helpful to develop monthly electricity purchase strategy.

关 键 词：自编码神经网络 消费者心理学 峰谷分时电价 叠加电价 

分 类 号：TM76[电气工程—电力系统及自动化] F407.61[经济管理—产业经济]